According to CIO, $3.2 billion was spent by companies on big data in 2010; it is predicted companies will spend $16.9 billion on big data by 2015

$300 billion could be saved if big data was used effectively the US healthcare sector; thereby reducing expenditure by 8%

Progressive Casualty Insurance Company, uses Big Data as part of its “pay as you drive” program, offers drivers the chance to lower their insurance premiums based on real-time analysis of their driving habits.

When we think about Big Data, we don’t typically directly associate it with money. To some extent, this is true; raw data alone is pretty much useless, and worthless. However, when it is analyzed and used properly, it can be an organization’s most valuable asset.

We all know that knowledge is power. And in the case of business, knowledge is profit. If you have enough information to determine how many staff members you need on the floor of a restaurant at all times, you can save a lot of money, and keep your customers happy. If you could identify how to store more supplies in a smaller safe, you could save real estate, and money. And if you can figure out what will keep your customers coming back time and time again, you will increase your profits.

To gain access to all this information, and to gain all this knowledge once seemed impossible. This is precisely why the terms “intuition,” and “business acumen” came about. But really these are just fancy terms for “educated guesses.” Data eliminates our need for guessing when making business decisions.

But of course, it’s not that easy. Just because a business or organization has access to data (every one does), does not mean that they can all monetize their data.

The first step to monetizing data is to consolidate that data. The term “big data” means that there is a lot of data, from many different sources. In order to understand the data and put it into context, you need to consolidate it all, to understand the whole picture.

Once it is consolidated, it needs to be properly analyzed. It is this analysis process that transforms the raw data into information.

But, you also need to make sure that this information is put in front of the right people. These people will transform the information into insight.

Then the insight can be used to make transformative business decisions.

So…monetizing Big Data isn’t exactly easy. But if you can do it, it will transform your business for the better. This is precisely why you need to hire the best data scientists you can, that truly understand the architecture of everything data. It is naive to think that you and your IT team can accomplish all of the above on your own – by the time you learned how to do it, it will have changed.

What is all the “Big Data Buzz” about? If collected, consolidated, managed and analyzed correctly, it gives us a unique ability to better understand the entire world around us. It helps us better understand the past, monitor the present and predict the future.

Here are the basics to understanding big data and how you can utilize its power to understand your business better, your health better, and even your personal life better.

3 Types of Data: Unstructured, Structured & Semi-Structured

Unstructured data we have previously explained in our blog. It is essentially data that does not reside in a fixed location, such as free-form text. An example of unstructured data is data from social media.

Structured data, in contrast, resides in fixed fields within a record or file. An example is an excel file.

Semi-structured data is data that is a combination of, or in between the two. This is where a structure, or “tags” are associated with or embedded into unstructured data.

Tools For Data Analytics

Hadoopis often used for job/task tracking for batch analytics processing.

ElasticSearch is an enterprise grade search.

Infinite is an analytic development environment platform to manage real-time analytic and search framework with customized visualization.

What Analytics Help Accomplish

Using the tools alone won’t get you too far. They typically are not an end-to-end solution, and require additional skills to utilize them. Teams of data scientists are able to use tools to create a customizable solution and approach to solving business problems.

They can take data from many different sources, to give you a full and complete picture of your business.

As a data analytics and business process engineering firm, we find that these 5 things are the most common challenges that businesses face. They also greatly impede on the prosperity of the company. Watch this video to find out what they are. And check out more on http://cliintel.com

It is no secret that Europeans have been extremely reluctant to jump on the Big Data train. As Americans continue to indulge in Big Data and incorporate it into their businesses, we must wonder, what will happen to Europe? Will European companies fall behind without the technology to even the playing field?

Despite what industry you are in, you can use data to achieve a wide variety of goals. It doesn’t matter if you are a big, or small business. Simply put, data analysis converts raw data into the accurate and up-to-date information you need to know what’s going on in every facet of your company.

Cancer is a horrible illness that has been nearly impossible to figure out. Scientists have had a difficult time finding a cure for cancer because no two cases of cancer are the same. Everyone’s bodies are different and therefore, those who develop cancer do not have identical cancer cells.

There is a glass with water in it sitting on a table- is the glass half empty or is it half full?

The way you answer the question above may determine how you think about IBM’s new solution for the high demand for data scientists. Data scientists are people who can analyze and control analytical programs that work to sort through large masses of data.

As the amount of data the world produces increases, the need for people who can sort through that data and make meaning of it goes up as well. IBM believes that they can aid this problem with their natural language analytics program which is predicted to be able to do the job of a data scientist.

This provokes the question, is this a good thing? Is this a means to an end or is this the beginning of a war between humans and robots for jobs?